Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study

Healthy nutrition contributes to preventing non-communicable and diet-related diseases. Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. However, knowledge about the effects of the long-term provision of health...

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Bibliographic Details
Published in:	User modeling and user-adapted interaction Vol. 32; no. 5; pp. 923 - 975
Main Authors:	Hauptmann, Hanna, Leipold, Nadja, Madenach, Mira, Wintergerst, Monika, Lurz, Martin, Groh, Georg, Böhm, Markus, Gedrich, Kurt, Krcmar, Helmut
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01-11-2022 Springer Nature B.V
Subjects:	Computer Science Customization Feedback Knowledge acquisition Management of Computing and Information Systems Mixed methods research Multimedia Information Systems Nutrition Qualitative analysis Recommender systems User Interfaces and Human Computer Interaction User experience Personalization Nutrition Health Recommender systems
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Summary:	Healthy nutrition contributes to preventing non-communicable and diet-related diseases. Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. However, knowledge about the effects of the long-term provision of health-aware recommendations in real-life situations is limited. This study investigates the impact of a mobile, personalized recommender system named Nutrilize. Our system offers automated personalized visual feedback and recommendations based on individual dietary behaviour, phenotype, and preferences. By using quantitative and qualitative measures of 34 participants during a study of 2–3 months, we provide a deeper understanding of how our nutrition application affects the users’ physique, nutrition behaviour, system interactions and system perception. Our results show that Nutrilize positively affects nutritional behaviour (conditional R 2 = . 342 ) measured by the optimal intake of each nutrient. The analysis of different application features shows that reflective visual feedback has a more substantial impact on healthy behaviour than the recommender (conditional R 2 = . 354 ). We further identify system limitations influencing this result, such as a lack of diversity, mistrust in healthiness and personalization, real-life contexts, and personal user characteristics with a qualitative analysis of semi-structured in-depth interviews. Finally, we discuss general knowledge acquired on the design of personalized mobile nutrition recommendations by identifying important factors, such as the users’ acceptance of the recommender’s taste, health, and personalization.
ISSN:	0924-1868 1573-1391
DOI:	10.1007/s11257-021-09301-y